A Pragmatic Review of AI in Telecommunication Network Optimization of 5G and Beyond: Enhancing Performance, Scalability, and Efficiency

Abstract

The review thoroughly examined the of Artificial Intelligence (AI) in telecommunications, specifically in enhancing network performance in 5G and emerging technologies. It investigated key advancements in network architecture, such as network slicing, edge computing, and the integration of heterogeneous networks. The review also highlighted how AI-based approaches address issues such as managing dynamic network traffic situations, ensuring quality of service, and optimising resource usage. It also evaluated AI approaches such as machine learning, deep learning, reinforcement learning, and federated learning approaches to determine their significance application and effectiveness in improving network performance, scalability, and efficiency. Additionally, the review analysed the benefits of using AI, to improve decision-making, dynamic adaptation, and automation, while acknowledging potential risks related to data privacy, security, and ethical concerns. Furthermore, the review presented an analysis of the implications of AI-driven optimization for the future of telecommunications, particularly in the development of network beyond 5G. The review concluded by reflecting on the revolutionary potential of AI in developing optimised, scalable, and sustainable networks that can meet the demands of the digital era.